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of blockchain, post-quantum cryptography, quantum algorithms, and heterogeneous quantum computing. Specifically, to achieve the main goal the project is expected to focus on three main areas: How can we support
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-edge optical microscopy systems for biomedical applications. This project involves the development of compact, label-free quantitative tomography systems and inverse scattering algorithms to push the
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of dynamic physical systems and control system algorithms experience in software development, data analysis and AI practical experience in experimental work motivation and potential for research within
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, in collaboration with the team responsible for the development of the ApneaScreener algorithm. Knowledge of data analysis tools, particularly Python and R, will be valued to support the interpretation
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modules, reasoning over structured graphs or rules, act as a factual verifier. The PhD fellow will perform the following tasks: Framework Design & Implementation, Reasoning Algorithms Development, and
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models; Participation in the implementation and execution of laboratory tests for validation of the platform's algorithms, interfaces, and subsystems; Support in the analysis of experimental results and in
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validation of artificial intelligence models. Support in the acquisition and management of data collected by the group in a clinical or laboratory setting. Experimental evaluation of algorithms, development
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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: - Consolidated knowledge in ECC and LDPC algorithms - Consolidated knowledge in hardware description languages and hardware-prototyping toolchains (e.g., Xilinx Vivado) - Knowledge in FPGA
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of novel satellite data analysis algorithms and solutions that will form the technology foundation for new products. The position is for a period of three years. Admission to the PhD programme is a